Multivariate perception testing for fire service thermal imager evaluations

Francine Amon; Dennis Leber; Justin Rowe

doi:10.1117/12.850312

22 April 2010 Multivariate perception testing for fire service thermal imager evaluations

Francine Amon, Dennis Leber, Justin Rowe

Proceedings Volume 7662, Infrared Imaging Systems: Design, Analysis, Modeling, and Testing XXI; 76620K (2010) https://doi.org/10.1117/12.850312
Event: SPIE Defense, Security, and Sensing, 2010, Orlando, Florida, United States

Abstract

This work provides an answer to the question "How good does the image need to be?" for testing image quality of fire service thermal imagers. Fire fighters were asked to identify potential fire hazards in 4500 images that had been degraded in brightness, contrast, spatial resolution, and noise level. A perception model was built from the resulting data. The methods of degrading the images used to develop the perception model were mathematically related to methods employed in objective laboratory-scale image quality testing. Thus, the perception model could be used to establish pass/fail criteria for objective laboratory-scale image quality tests of nonuniformity, spatial resolution, and effective temperature range for fire service thermal imagers. The perception model was applied to images that were collected using a high resolution visible camera focused on the thermal imager's display while the thermal imager viewed a variety of thermal targets. In this way, the subjectivity of human perception testing is applied equally to all thermal imagers being tested for compliance to a nationally standardized set of image quality tests. As fire service imaging needs and test methods evolve, the perception testing can be updated with different image types and scenarios.

Citation Download Citation

Francine Amon, Dennis Leber, and Justin Rowe "Multivariate perception testing for fire service thermal imager evaluations", Proc. SPIE 7662, Infrared Imaging Systems: Design, Analysis, Modeling, and Testing XXI, 76620K (22 April 2010); https://doi.org/10.1117/12.850312

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KEYWORDS

Image quality

Thermography

Imaging systems

Thermal modeling

Spatial resolution

Data modeling

Temperature metrology

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